Sashimi Plots: An Essential Tool for Visualizing Gene Splicing and Expression in Bioinformatics
In RNA sequencing (RNA-seq) and bioinformatics, visualising complex gene structures and splicing patterns is essential for interpreting data and drawing meaningful conclusions. One of the most powerful visualisation tools researchers rely on is the sashimi plot. Whether examining alternative splicing in cancer, exploring gene expression across tissues, or analysing RNA structures, sashimi plots are indispensable. This article dives into what sashimi plots are, how they work, and why they’re invaluable in genomic research.
What is a Sashimi Plot?
A sashimi plot is a specialised graph for visualising RNA-seq data, focusing on splicing events and exon-intron structures within genes. It “slices” through RNA-seq data, allowing researchers to examine gene structure details quickly and easily. Sashimi plots integrate two core components:
Coverage Data: Displays the number of sequencing reads mapping to specific exons within a gene, giving a clear view of expression levels.
Junction Reads: Highlights reads spanning exons, showing where splicing occurs and connecting the gene’s functional segments.
In a sashimi plot:
Exons appear as thick horizontal blocks.
Introns are thin lines connecting exons.
Splice junctions are illustrated as arcs, often annotated with read counts to show splice event frequency.
These features make sashimi plots ideal for understanding gene structures and splicing events. They clearly show gene expression and splicing patterns across different conditions.
Applications of Sashimi Plots
Sashimi plots are widely used to examine alternative splicing, where different exon combinations produce multiple RNA transcripts from a single gene. Key applications include:
Comparing Gene Expression and Splicing Patterns
Researchers use sashimi plots to compare gene expression and splicing across different conditions, such as healthy vs. cancerous tissue. This comparison can reveal how splicing changes may contribute to disease mechanisms or therapeutic responses.
Exploring Tissue-Specific and Developmental Splicing
Specific genes show unique splicing patterns depending on tissue type or developmental stage. Sashimi plots help identify tissue-specific transcripts, providing insights into genes' specialised roles in different biological contexts.
Validating Differential Splicing Events
Sashimi plots often validate significant splicing events identified during RNA-seq analysis. By visualising these events, researchers can confirm that observed splicing patterns are consistent and biologically relevant.
How Sashimi Plots Work
Creating a sashimi plot from RNA-seq data involves several steps:
Aligning RNA-seq Reads
RNA-seq reads are aligned to a reference genome using tools like STAR or HISAT2. These tools map reads to exons and identify reads spanning exon-exon boundaries, showing splice junctions.
Counting Reads and Identifying Junctions
Reads are counted for each exon and junction. Exon read coverage provides expression levels, while junction reads show which exons are spliced together.
Visualising with Sashimi Plots
The read data is then visualised in a sashimi plot: exons as thick blocks with coverage data and arcs between exons representing splice junctions. The arc size typically reflects the number of junction reads, visually indicating splicing frequency.
Tools for Generating Sashimi Plots
Several bioinformatics tools support sashimi plot creation:
IGV (Integrative Genomics Viewer): A widely used tool for generating basic sashimi plots with exon-intron structures and splice junctions.
ggsashimi: A Python package for creating customisable sashimi plots with options for colour, labels, and data scales.
MISO (Mixture of Isoforms): A probabilistic framework that includes sashimi plotting as part of its RNA-seq analysis toolkit.
These tools allow researchers to produce sashimi plots highlighting specific splicing events or gene expression patterns relevant to their studies.
Benefits of Sashimi Plots
Sashimi plots offer a multi-layered view of splicing and expression data, making them invaluable in bioinformatics:
Detailed View of Alternative Splicing: They highlight exon skipping, intron retention, and alternative splice sites.
Insights into Gene Expression Patterns: Exon coverage data provides insight into relative gene expression levels.
Comparison Across Conditions: Researchers can compare splicing across samples, such as between healthy and diseased tissues, revealing condition-specific transcript variants.
Challenges and Limitations of Sashimi Plots
Despite their strengths, sashimi plots have a few limitations:
Sample-Specific Variability: RNA-seq data can vary across samples, and low read counts can reduce visualisation clarity.
Complex Interpretation for Multi-Isoform Genes: Plots for genes with multiple isoforms or complex splicing can become cluttered, requiring expert knowledge for accurate interpretation.
Resource-Intensive for Large Datasets: Generating high-quality sashimi plots for large datasets may require significant computational resources.
Sashimi Plots in Action
For researchers in bioinformatics, sashimi plots offer a practical visual method for interpreting splicing and gene expression data. From exploring disease mechanisms to studying tissue-specific expression, sashimi plots help untangle complex RNA structures, advancing our understanding of gene regulation and function.
Final Thoughts
With the continued evolution of RNA-seq and bioinformatics, sashimi plots remain foundational tools for visualising gene expression and splicing. By presenting exon structures, introns, and splice junctions in a single, unified view, they empower scientists to make informed conclusions about gene function and regulation.
Though named after a Japanese delicacy, sashimi plots are now a staple in the toolkit of bioinformaticians. Whether you’re just starting in the field or are an experienced researcher, sashimi plots provide a clear, accessible way to explore the intricate world of gene splicing and expression.
To see sashimi plots in action and explore specific examples of splicing events across different conditions, check out our collection of splicing case studies on our website.